A study on speaker identification based on weighted LS-SVM

Abstract

As one of a practical method, least squares support vector machine (LS-SVM) is usable for nonlinear separable problem as speaker identification. However, single LS-SVM can only do such classifying as binary classification, so it always needs multiple LS-SVMs and corresponding algorithms for classifying multiple speakers in a speaker identification database. By comparing pairwise LS-SVM with one-against-all LS-SVM, it is obvious that the pairwise LS-SVM has the advantage of facilitative expanding for different cases, while the one-against-all LS-SVM can not bring. However conventional pairwise LS-SVM needs too many judgment times to do multi-classing. In order to improve the pairwise LS-SVM and make it applicable to multi-speaker identification system, we propose a new notion of classification weight for pairwise LS-SVM and the corresponding algorithm, named as pairwise LS-SVM based on classification weight, i.e., the m-ωLS-SVM method, which can be used in multi-speaker identification system. Experiment results show that, comparing with conventional pairwise LS-SVM, the identification speed of the system with m-ωLS-SVM method is improved while keeping correct rate of identification, or vice versa, with only a little increase of training time.